CN114779673A - Power pipe gallery sensing monitoring method based on screening and control signal optimization - Google Patents
Power pipe gallery sensing monitoring method based on screening and control signal optimization Download PDFInfo
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Abstract
The invention discloses a power pipe gallery sensing monitoring method based on screening signal optimization, which is characterized in that the screening signal at least adopts optimization steps of generating a screening signal, constructing a primary test model and a secondary test model, preliminarily verifying the screening signal, carrying out secondary test verification on the screening signal, optimizing the screening signal to obtain a target control signal, optimizing the screening signal to obtain the target control signal and carrying out secondary test verification on the target control signal, so as to generate a final target control signal, and sending the final target control signal to a sensing monitoring system of a power pipe gallery for early warning and prompting. The invention is suitable for the compound monitoring management of the power pipe gallery and has the remarkable advantages of high reliability, small misjudgment rate, high early warning management efficiency and the like.
Description
Technical Field
The invention belongs to a power pipe gallery monitoring system in the technical field of new generation information, and particularly relates to a power pipe gallery sensing monitoring method based on screening signal optimization.
Background
Along with the rapid development of urban modern construction, the construction of urban underground electric power pipe galleries extends to each corner of a city, and meanwhile, the construction of the electric power pipe galleries between the inter-city walls is rising. The power pipe gallery comprises a cable trench, a cable pipe bank, a cable tunnel, GIL (short for gas insulated metal enclosed transmission line) or a combination of the two, and the safe operation of the power pipe gallery is an important guarantee for the safe and stable operation of cities and even national power grids. But because of the direct influence of the power pipe gallery in the aspects of uneven underground environmental conditions and the like, the requirement on the operation and maintenance of the power pipe gallery is continuously improved. At present, in the actual construction engineering of the urban underground electric power pipe gallery, only the conventional electric power pipe gallery monitoring and management system is additionally arranged, and the reliability and the stability of the existing system can not meet the actual requirements of safe and stable operation of the urban and even national power grid.
The Chinese patent application (CN104778541A) discloses an electric power underground pipe network management control system, which comprises an electric power pipeline equipment basic information acquisition unit, an electric power pipeline equipment running state monitoring unit, an electric power pipeline equipment management unit, an electric power pipeline equipment running state analysis unit, an electric power pipeline equipment fault alarm unit and an electric power underground pipe network geographic information comprehensive display unit.
The Chinese patent application (CN205384471U) discloses an electric power underground pipe network management control system, which can adopt the maintenance equipment in the fault equipment area to carry out maintenance work in time when monitoring that the equipment has a fault through the cooperation of an information acquisition module, an operation state analysis module, a feedback module, a management unit and a maintenance module, and can also be matched with a mobile terminal to automatically complete alarm work and the like.
Chinese patent application (CN213846333U) discloses a GIL line monitoring system, which comprises a central processing unit, a first-level monitoring unit and a second-level monitoring unit, wherein the first-level monitoring unit and the second-level monitoring unit comprise a plurality of detection points, the detection points are arranged along a GIL line, the first-level monitoring unit acquires gas density, temperature and humidity information of the GIL, the second-level monitoring unit acquires voltage and current information of the GIL, the first-level monitoring unit and the second-level monitoring unit are connected with the central processing unit through a network, and the central processing unit is further connected with a human-computer interaction unit. Compared with the traditional primary monitoring unit for voltage and current, although the secondary monitoring unit for monitoring the density, temperature and humidity of the inert gas of the GIL is added in the system, the accidents and the like of the GIL line can be quickly detected, the two-stage monitoring mode of the system does not change the essence of the one-way monitoring management method, and the system has no rechecking and screening functions, so that the misjudgment rate is high, and the reliability is low.
In summary, how to overcome the defects of the technical scheme of the existing power pipe gallery monitoring system becomes one of the key problems to be solved urgently in the field of the information technology of the new generation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power pipe gallery sensing monitoring method based on screening signal optimization.
The invention provides a power pipe gallery sensing monitoring method based on screening signal optimization, which is characterized in that the screening signal at least comprises the following optimization steps to generate a final target control signal which is sent to a sensing monitoring system of a power pipe gallery for early warning and prompting according to the correlation characteristics of a power pipe gallery and the target sensing data of the sensing monitoring system:
and S1, generating a screening signal: operation of collected power pipe galleryProcessing target sensing data of the dimension monitoring system, and synchronously generating corresponding main control signals by adopting a main control algorithm and a complex control algorithm which are mutually independent
And a complex control signal
Sending the master control signal and the re-control signal to an operation and maintenance monitoring system of the power pipe gallery; in the formula (I), the compound is shown in the specification,
for first sensed data received by the first control module over the first communication link,
second sensor data received for a second control module via a second communication link, F1(. is a master control algorithm built into the first control module, F2() is a complex control algorithm built in the second control module;
s2, constructing an initial test model and a review test model: correcting the inherent error parameters of the main control algorithm and the repeated control algorithm according to the relevant characteristics of the power pipe gallery, and constructing an initial test model and a repeated test model by combining the correction results;
s3, carrying out preliminary verification on the screening signal: the first sensing data in the current auditing period T is checked
Second sensing data
And a re-control signal B (T) is led into the initial test model, so that the initial test model calls a master control algorithm to carry out the second sensing data
Performing operation to obtain a single-core control instruction A' (T), and controlling the single core by combining the corrected allowable error threshold value of the master control algorithmAnalyzing the command A' (T), the main control signal A (T) and the reset control signal B (T), and calculating to obtain the self error delta epsilon of the main control algorithmA(T) and the error between the master control algorithm and the re-control algorithmAB(T)；
S4, performing double-check verification on the screening control signal: collecting the first sensing data in the current auditing period T
Second set of sensory data
And leading the master control signal A (T) into a review model, so that the review model calls a review control algorithm to the first sensing data
Calculating to obtain a rechecking control instruction B' (T), and calculating to obtain the self error delta epsilon of the rechecking algorithm by combining with the error parameter of the rechecking algorithm after correctionB(T) judging the reliability of the screening signal, if the screening signal is reliable, entering the step S5, otherwise, generating an early warning signal, and ending the process;
s5, optimizing the discrimination control signal to obtain a target control signal: self error delta epsilon combined with master control algorithmA(T), error between master control algorithm and multiple control algorithmAB(T), self error Delta epsilon of complex control algorithmBAnd (T) estimating the error trend of the main control signals A (T) and the complex control signals B (T), and calculating to obtain the target control signal according to the estimation result.
The realization principle of the invention is as follows: the invention provides a power pipe gallery sensing monitoring method based on screening signal optimization, which is provided by the applicant on the basis of repeated experimental demonstration and the important difficult problem to be solved urgently in the construction engineering of an underground power pipe gallery. The scheme is based on the relevant characteristics of the power pipe gallery and the target sensing data of the sensing monitoring system, and adopts a compound accurate screening signal optimization method, namely an optimization method of a target screening signal under the condition that a master control algorithm and a complex control algorithm are in a normal state, so that two mutually independent screening signals with the complex inspection characteristics are synchronously generated, even if the master control signal and the complex control signal fluctuate, the corresponding error threshold value is not exceeded, the finally obtained target control signal can be closer to the standard signal of the current operation and maintenance period, and then the target control signal is sent to the sensing monitoring system of the power pipe gallery for early warning and prompting, so that the operation and maintenance of the power pipe gallery can be timely performed with safety measures, and the actual requirements of safe and stable operation of the urban and even national power grid are met.
Compared with the prior art, the invention has the remarkable advantages that:
firstly, the power pipe gallery sensing monitoring method based on screening and control signal optimization integrates the sensing monitoring, the main control rechecking and the rechecking functions, is scientific, reasonable and practical in scheme design, can effectively solve the problems of large misjudgment rate, low reliability and poor practicability of the existing power pipe gallery operation and maintenance one-way sensing monitoring method, and improves the technical progress level in the field to a new development stage.
Secondly, the power pipe gallery sensing monitoring method based on screening signal optimization can carry out complex precise screening management on a power pipe gallery and an operation and maintenance monitoring system, particularly two mutually independent screening signals with a retest characteristic are synchronously generated by the method for carrying out complex precise screening optimization on the relevant characteristics of the power pipe gallery and target sensing data of the operation and maintenance monitoring system, and the target sensing data are sent to the operation and maintenance monitoring system of the power pipe gallery for early warning prompt through complex verification of the target screening signals.
Thirdly, the power pipe gallery sensing monitoring method based on screening signal optimization is not only suitable for monitoring management of operation and maintenance of newly-built urban underground power pipe galleries, but also suitable for technical improvement and upgrading of an existing urban underground power pipe gallery operation and maintenance monitoring system.
Drawings
Fig. 1 is a schematic flow chart of a power pipe gallery sensing monitoring method based on screening signal optimization according to the present invention.
FIG. 2 is a schematic diagram of an initial flow of the first control module according to the present invention.
FIG. 3 is a schematic diagram of a review flow of the second control module of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings and examples.
Fig. 1 is a flowchart of a power pipe gallery sensing monitoring method based on screening signal optimization according to the present invention. The control optimization method at least adopts the following optimization steps to the discrimination signals according to the relevant characteristics of the power pipe gallery and the target sensing data of the sensing monitoring system, generates final target control signals and sends the final target control signals to the sensing monitoring system of the power pipe gallery for early warning prompt:
and S1, generating a screening signal: processing the collected target sensing data of the operation and maintenance monitoring system of the power pipe gallery, and synchronously generating corresponding main control signals by adopting a main control algorithm and a multiple control algorithm which are independent of each other
And a complex control signal
Sending the master control signal and the re-control signal to an operation and maintenance monitoring system of the power pipe gallery; in the formula (I), the compound is shown in the specification,
for the first sensed data received by the first control module over the first communication link,
for the second control module via a second communication linkReceived second sensed data, F1(. is a master control algorithm built into the first control module, F2(. cndot.) is a complex control algorithm built in the second control module.
S2, constructing an initial test model and a review model: and correcting the inherent error parameters of the main control algorithm and the multiple control algorithm by combining the relevant characteristics of the power pipe gallery, and constructing an initial test model and a multiple test model by combining the correction result.
S3, carrying out preliminary verification on the screening signal: the first sensing data in the current auditing period T is checked
Second sensing data
And the reset control signal B (T) is led into the first test model, so that the first test model calls the master control algorithm to carry out comparison on the second sensing data
Calculating to obtain a single-core control instruction A '(T), analyzing the single-core control instruction A' (T), the main control signal A (T) and the complex control signal B (T) by combining the corrected allowable error threshold of the main control algorithm, and calculating to obtain the self error delta epsilon of the main control algorithmA(T), and the error between the master control algorithm and the override algorithm, Delta epsilonAB(T)。
S4, performing double-check verification on the screening control signal: collecting the first sensing data in the current auditing period T
Second set of sensory data
And leading the master control signal A (T) into a review model, so that the review model calls a review control algorithm to the first sensing data
Performing operation to obtain a double check control instruction B' (T) calculating the self error delta epsilon of the complex control algorithm by combining the corrected error parameters of the complex control algorithmBAnd (T) judging the reliability of the screening signal, if the screening signal is reliable, entering the step S5, otherwise, generating an early warning signal, and ending the process.
S5, optimizing the screening signal to obtain a target control signal: self error delta epsilon combined with master control algorithmA(T), error Delta epsilon between master control algorithm and multiple control algorithmAB(T), self error Delta epsilon of complex control algorithmBAnd (T) estimating the error trend of the main control signals A (T) and the complex control signals B (T), and calculating to obtain the target control signal according to the estimation result.
In this embodiment, the relevant characteristic of electric power pipe gallery and sensing monitored control system are the design scheme original appearance among the current electric power pipe gallery construction engineering, and this embodiment need not do any substantive technical improvement to electric power pipe gallery and sensing monitored control system among the prior art. In other words, the accurate discrimination method for the power pipe gallery disclosed by the embodiment can be directly popularized and applied to the safety management engineering of the power pipe gallery.
The following explains in detail a preferred embodiment of the power pipe gallery sensing monitoring method based on screening signal optimization disclosed in this embodiment with reference to the accompanying drawings.
Control layer
In order to achieve the compatible effect of master control and re-control, the following two data transmission channels are designed in this embodiment: the first control device and the monitoring sensing system form main control sensing signal connection; the second control device and the monitoring sensing system form a complex control sensing signal connection. When the sensing monitoring system of the power pipe gallery collects new sensing data, the collection result is sent to the first control device and the second control device through the two data transmission channels respectively. The source data received by the first control device and the second control device are the same, but eventually, there may be some differences in the received sensing data packets due to different transmission channels, such as a small amount of data missing. This is a communication environment by the power pipe galleryThe decision, because electric power piping lane buries underground usually, the electric power facility is intensive, and network transmission signal is poor, especially along with the rapid development of thing networking and light-weight sensing supervisory equipment's extensive implementation, in order to realize real time control, need directly send the sensing data of gathering to corresponding monitoring module, some data mistransmission or neglected transmission etc. can appear in the transmission course unavoidably. For the purpose of distinction, the sensor data received by the first control device is defined as first sensor data
Defining the sensing data received by the second control device as second sensing data
Wherein t is the acquisition time.
Referring to fig. 2, the first control device receives the first sensing data at the t-th time sent by the sensing monitoring system
Checking, and calling a built-in master control algorithm to the first sensing data after the first sensing data is checked to be correct
Operating to generate a corresponding main control signal A (t), and storing the first sensing data in a certain time period by adopting a first data storage submodule
And corresponding master control signals a (t) to facilitate traceability and auditing.
Meanwhile, the second control device receives the sensing data at the t moment sent by the sensing monitoring system
Calling a built-in re-control algorithm to second sensing data after checking to be correct
Operating to generate corresponding control signals B (t), and storing the second sensing data in a certain period of time by adopting a second data storage submodule
And a corresponding complex control signal b (t).
Illustratively, the master control algorithm and the multiple control algorithm may be the same or different. Adopt host system and compound accuse module to carry out one of them purpose of controlling to the power pipe gallery simultaneously and be: the sensing monitoring system can achieve a more accurate control effect by means of the main control signal and the complex control signal, namely, mutual verification and mutual assistance effects exist between the main control signal and the complex control signal.
According to the relevant characteristics of the target control equipment of the power pipe rack, the main control signal A (t) sent by the first control device and the complex control signal B (t) sent by the second control device can be divided into a control signal aiming at the switch equipment and a control signal aiming at the regulation and control equipment. Taking an exhaust fan as an example, a switch module of the exhaust fan belongs to switch equipment, and a corresponding control signal is on or off; a power adjusting module of the exhaust fan belongs to regulation and control equipment, and a control signal is a target power parameter.
Therefore, the present embodiment is set as follows:
the main control signal a (t) and the complex control signal b (t) received by the sensing monitoring system are respectively defined as:
a in formula (1)i(t) and b in the formula (2)i(t) is a control signal sent by the first control module and the second control module for the ith switching device, i is 1, 2j(t) and betaj(t) a first control module and a second control module, respectivelyAnd sending a control signal j which is 1, 2, for the j-th regulation and control equipment.
(II) audit level
The exception determination between the master control signal and the reset control signal is typically a determination of an incumbent fact. In order to realize the early warning effect, the embodiment provides an initial check process and a review process. Referring to fig. 1, the reliability of the master control signal and the reliability of the multiple control signal are monitored by constructing an initial test model and a multiple test model.
(2.1) Signal alignment function
In this embodiment, multiple calls of the signal to the function g (-) are involved. The signal comparison function g (-) is mainly used for comparing two groups of control signals with certain difference so as to judge whether the object with difference has a problem.
Illustratively, the signal alignment function g (-) is:
t ∈ T, ρ in the formula (4) or (5)i(t) control signal for the ith switching device, ξ, which is the first comparison signalj(t) a control signal for the jth regulation class device that is the first comparison signal; sigmai(t) a control signal for the ith switching class device, ζ, which is the second comparison signalj(t) is a control signal of the second comparison signal for the jth regulation and control class device; epsilon is the maximum allowable error value of two comparison signals for the jth regulation and control equipment; when the values of g (P), (T), Q (T) and epsilon) are less than 1, the two comparison signals are judged to be matched, and the larger the values of g (P), (T), Q (T) and epsilon are, the larger the error is.
P (T), Q (T) are two groups of control signals with difference, in this embodiment, the main factors causing the difference of the control signals include different sensing data and non-control algorithmThe two types are the same. The function of the signal comparison function g (-) is to compare whether the difference degree between the two control signals exceeds the corresponding allowable error threshold value or not, and then the main control algorithm F is subjected to comparison1And complex control algorithm F2The operating state of (c) is judged.
(2.1) construction of Primary and Compound models
The construction process of the first-time model and the second-time model comprises the following steps: and correcting the inherent error parameters of the main control algorithm and the re-control algorithm by combining the relevant characteristics of the power pipe gallery, and constructing a single-core model and a re-core model by combining the correction result.
In particular, associated characteristics Y in combination with the corresponding power line corridorq(T) Pair Master control Algorithm F1And complex control algorithm F2Correcting the respective allowable abnormal standard value to generate a corresponding mononuclear abnormal standard value muD(T) and the standard value of the rechecking abnormality μS(T). Exemplarily, the correlation characteristic Y of the power pipe lane qq(T) is: y isq(T)＝{Eq(T)，ρq(T)，Γq(T)，τq(T) }; in the formula, q is the number of the power pipe gallery; eq(T) is the influence value of the power pipe gallery in the Tth audit period; ρ is a unit of a gradientq(T) is the number of target facilities in the power pipe gallery in the Tth audit period; gamma-shapedq(T) is the time length of putting the power pipe gallery into operation until the Tth audit period; tau isqAnd (T) is the interval duration of last maintenance of the power pipe gallery until the Tth audit period. For example, a certain power pipe rack is connected with more public facilities, wherein a large number of important public facilities are not lacked, the influence value of the power pipe rack is higher, and in order to ensure that the public facilities can always maintain a normal operation state, the allowable abnormal standard value can be properly reduced. Correspondingly, the higher the influence value of the power pipe gallery, the larger the number of the target facilities in the power pipe gallery, the shorter the time length for putting the power pipe gallery into operation, the shorter the time length of the last maintenance interval of the power pipe gallery, and the smaller the allowable abnormal standard value. The aforementioned characteristic parameters of the power pipe lane are dynamically changed, and therefore, the process of correcting the allowable abnormal standard value also needs to be periodically performed.
(2.3) first test verification
Referring to fig. 2, the process of performing preliminary verification on the screening signal includes the following steps:
s301, requesting the second control module to feed back the received second sensing data in the appointed auditing period
S302, second sensing data is processed
The first sensing data received by the first control module in the same auditing period
Comparing, and evaluating the communication quality of the first communication link according to the comparison error; if the comparison error between the two meets a preset comparison error threshold value, calling a master control algorithm to perform comparison on the second sensing data
Processing to generate corresponding single-core control instruction
Entering step S303; otherwise, directly outputting the communication quality evaluation result of the first communication link as an initial test result D (T) and ending the single-core process.
S303, calling a signal comparison function g (-) to perform matching calculation on the single-core control instruction A' (T) and the main control instruction A (T) in the same auditing period to obtain the self error delta epsilon of the main control algorithmA(T)＝g(A′(T)，A(T)，εjA)；εjAIs the allowable error threshold of the modified master algorithm.
S304, calling a signal comparison function g (-) to compare and analyze the single-core control instruction A' (T) and the complex control instruction B (T) in the appointed auditing period to obtain an error delta epsilon between the main control algorithm and the complex control algorithmAB(T)＝g(A′(T)，B(T)，εjAB)；εjABIs the allowable error between the corrected main control algorithm and the complex control algorithmAnd (4) a threshold value.
S305, integrating the communication quality evaluation result of the first communication link in the current auditing period T and the self error delta epsilon of the main control algorithmA(T) and error between master control algorithm and multiple control algorithmAB(T), generating an initial test result D (T).
The judgment process of the initial test result comprises the following steps:
evaluating the communication quality of the first communication link and the self error Delta epsilon of the master control algorithmA(T) and error between master control algorithm and double control algorithmAB(T) comparing the three verification item data with corresponding preset standards: if the three verification item data meet corresponding preset standards, the reliability of the main control signal A (t) and the reliability of the reset control signal B (t) are judged to be maintained stably; if the communication quality evaluation result of the first communication link or the error stability of the master control algorithm does not meet the corresponding preset standard, judging that the reliability of the master control signal A (t) is in a greatly reduced trend; for other situations, comprehensive judgment needs to be carried out by combining the result of the double-check.
Specifically, as for the communication quality evaluation result of the first communication link, when the communication quality evaluation result of the first communication link is poor, it may be considered that the reliability of the main control signal a (t) is in a greatly reduced trend. Different from the situation that the sensing data are directly sent to the first control module and the second control module in the real-time control process, the accuracy of data transmission between the first control module and the second control module is more inclined due to the fact that the requirement of the first check process (and the second check process) on the real-time performance is not high, and for example, the accurate transmission of the data between the first control module and the second control module is achieved by means of encryption, summary check and the like.
Self error Δ ε for master control algorithmA(T), the adopted control algorithms are all master control algorithms F1A (T) is a master control algorithm F1For the first sensing data
The calculation is obtained, A' (T) is the main control algorithm F1For the second sensing data
Operated on to obtain first sensed data
And second sensing data
Is the same, so the self-error of the master algorithm, Δ ∈, can be calculatedA(T) for auditing the Master control Algorithm F1Is robust when Δ εA(T) when the value is too large, the main control algorithm F is explained1The sensitivity to data fluctuation is extremely high, and the first sensing data received in real time
Major deviation occurs, and the main control algorithm F1I.e. it is possible to generate a control signal with a large error value. For this situation, it can be known that the reliability of the master control algorithm is reduced only through the first-pass model, and in order to continuously maintain the control of the power pipe lane, an early warning signal can be generated to request operation and maintenance. It should be noted that the initial test model and the repeated test model are only used for monitoring and early warning the reliability trend of the main control algorithm and the repeated control algorithm, and even if the reliability of one of the main control algorithm and the repeated control algorithm is reduced, the main control signal and the repeated control signal output by the main control algorithm and the repeated control algorithm are still credible in a short time, so that the embodiment can ensure accurate monitoring of the power pipe gallery.
For the error between the master control algorithm and the re-control algorithmAB(T), the processed data objects are all the second sensing data
The difference lies in that the adopted control algorithm is different: a' (T) is the master control algorithm F1Operation is obtained, B and T adopt a complex control algorithm F2And (6) obtaining the result through calculation. Thus, the error Δ ∈AB(T) can be used to audit master control algorithm F1And complex control algorithm F2Actual error between the output results. When the master control algorithm F1And multiple control algorithm F2The same causes of errorsElements are limited to performance parameters of the computing device, when the master control algorithm F1And multiple control algorithm F2When different, the influencing factors causing the error include the performance parameters of the computing device and the differences between the two control algorithms themselves. When the error between the main control algorithm and the complex control algorithm is delta epsilonABWhen the numerical value of (T) is too large, the main control algorithm F is explained1And multiple control algorithm F2At least one of which has a large operational deviation or an anomaly of the maximum allowable error between the two. No matter the self error delta epsilon of the master control algorithmAHow to judge the reliability of the main control signal a (T) and the repeated control signal b (T) cannot be determined, and comprehensive judgment needs to be performed by combining the repeated test results.
(2.4) double-check verification
Referring to fig. 3, the process of performing a double-check verification on the screening signal includes the following steps:
s311, the first control module is requested to feed back the received first sensing data in the appointed auditing period
S312, the first sensing data is processed
With the second sensing data of the same audit period
Comparing, evaluating the communication quality of the second communication link according to the comparison error, and calling a re-control algorithm to the first sensing data if the comparison error between the first communication link and the second communication link meets a preset comparison error threshold value
Processing to generate corresponding double-check control instruction
The flow advances to step S313; otherwise, directly outputting the communication quality evaluation result of the second communication link as a re-checking result FAnd (T) ending the rechecking process.
S313, calling a signal comparison function g (-) to perform matching calculation on the double-check control instruction B' (T) and the double-control instruction B (T) in the same audit period to obtain the self error delta epsilon of the double-control algorithmB(T)＝g(B′(T)，B(T)，εjB)；εjBIs the allowable error threshold of the modified complex control algorithm.
S314, using a master control algorithm F1And multiple control algorithm F2Maximum allowable error value epsilon ofjBARandomly generating a quantitative error sensing data to part of the first sensing data for evaluation
Replacing to obtain standard calibration data
T＝{t1，t1，...，tK}，tkIs the kth acquisition time node of the current audit period T, K is 1, 21，t1，...，tK}。
S315, respectively calling the main control algorithm and the repeated control algorithm to check the standard data
Processing the signal to generate a check reset signal A*(T) and a check reset signal B*(T)。
S316, calling the single-core module to each acquisition time node tkCheck-up reset control signal A*(tk) and check reset control signal B*(tk) Comparing, analyzing and obtaining the position information Y epsilon { t } of the error sensing data1，t1，...，tKAnd matching the position information Y with the replacement position information X to obtain a matching result f2(t)。
S317, combining the communication quality evaluation result of the second communication link and the self error delta epsilon of the repeated control algorithmB(T) and matching result f2(t) generating a review result F (T).
The communication quality evaluation result of the second communication link and the self error delta epsilon of the re-control algorithm are obtainedB(T) and matching result f2And (t) comparing the three verification item data with corresponding preset standards, judging that the second control module operates normally when the three verification items are normal, and otherwise, generating an early warning signal. Illustratively, if the self-error of the master algorithm is Δ εA(T), self error Delta epsilon of complex control algorithmB(T) and matching result f2(t) meets the corresponding preset criteria, but the error between the master control algorithm and the multiple control algorithm is delta epsilonAB(T) if the predetermined criterion is exceeded, it can still be determined that a stable error exists between the main control signal a (T) and the reset control signal b (T) within the allowable range. For example, when the master signal a (t) is slightly larger and the reset signal b (t) is slightly smaller, the error between the two signals becomes larger, but it does not indicate that the master signal a (t) and the reset signal b (t) are not available.
Specifically, as for the communication quality evaluation result of the second communication link, when the communication quality evaluation result of the second communication link is poor, it can be considered that the reliability of the multiple control signal a (t) is in a greatly decreasing trend.
Self-error delta epsilon for complex control algorithmB(T), the adopted control algorithms are all complex control algorithms F2B' (T) is a complex control algorithm F2For the first sensing data
The operation is obtained, B (T) is the complex control algorithm F2For the second sensing data
Operated on to obtain first sensed data
And second sensing data
Is the same, and can be calculated to obtain a complex control algorithmError stability F1(T) for checking the repetitive control algorithm F2When Δ ε is improvedB(T) when the value is too large, the complex control algorithm F is explained2The sensitivity to data fluctuation is extremely high, and the second sensing data is received in real time
Major deviation occurs, and the main control algorithm F1I.e. it is possible to generate erroneous control signals.
For the matching result f2(t), then for Master control Algorithm F1And complex control algorithm F2The maximum allowable error value therebetween. Specifically, a master control algorithm F1And multiple control algorithm F2Maximum allowable error value epsilon ofjBARandomly generating a certain amount of error sensing data to part of the first sensing data for judging the basis
Replacing to obtain standard check data
The generated part of error sensing data meets the main control algorithm F1And multiple control algorithm F2The error requirement of the device is self, but the error requirement between the two is not met.
Then will verify the data according to the standard
Generated per acquisition time node tkCheck-up of (A) complex control signal*(tk) And check complex control signal B*(tk) Comparing, analyzing and obtaining the position information Y e { t of the error sensing data1，t1，...，tK}. Theoretically, if the maximum permissible error value ε is usedjBAThe use requirements are met, and wrong control signals which possibly cause wrong operation of the power pipe gallery and corresponding position information of wrong sensing data can be accurately found out; in other words, if part or even most of the erroneous sensing data is missing, the data is processedIllustrates the maximum allowable error value epsilonjBAAn anomaly has occurred or the current demand is no longer adapted. The comparison of the check signals can be performed by an initial test model or a review test model. In this embodiment, the review process is both a supplement to the first-time result and a recheck of the reliability of the first-time result.
(IV) control optimization method
And assuming that the error between the master control signal and the multiple control signal output by the master control algorithm and the multiple control algorithm after each operation and maintenance can be ignored, and the error between the master control signal and the multiple control signal can be defaulted to be the same as the standard signal. In the automatic operation process, the main control algorithm and the multiple control algorithm gradually have errors due to the algorithm itself, and in order to make the final target control signal closer to the initial standard signal, the embodiment judges the error trend of the main control algorithm and the multiple control algorithm.
Specifically, in step S5, the process of estimating the error trend of the main control signal a (t) and the complex control signal b (t) includes the following steps:
s51, fitting the main control signal A (T), the complex control signal B (T), the single-core control instruction A '(T) and the complex control instruction B' (T) of the current auditing period T by taking time as a horizontal axis and taking the signal normalization value as a vertical axis to obtain a corresponding fitting curve YA(T)、 YB(T)、YA′(T) and YB′(T)。
S52, combining the fitting curve YA(T)、YB(T)、YA′(T) and YB′And (T) estimating the error trend of the main control algorithm and the repeated control algorithm.
In step S52, the fitted curve Y is combinedA(T)、YB(T)、YA′(T) and YB′(T) estimating the error trend of the main control algorithm and the complex control algorithm by the position relation of (T) comprises the following steps:
selecting two fitting curves positioned in the middle position, and analyzing the corresponding control algorithm:
if the control algorithms corresponding to the two fitting curves in the middle position are both master control algorithms, judging that the error between the master control signal and the standard signal is smaller; if the control algorithms corresponding to the two fitting curves positioned in the middle position are both complex control algorithms, judging that the error between the complex control signals and the standard signals is smaller; otherwise, the error trend between the main control signal and the standard signal and the error trend between the complex control signal and the standard signal are opposite.
In practical application, the error fluctuation rules of the master control algorithm and the complex control algorithm can be judged according to the position relation of the four curves, for example, a fitting curve YA(T) is located higher than Y on the longitudinal axisA′(T) but fitting the curve YB′(T) is located lower than Y on the longitudinal axisB(T), the main control algorithm and the multiple control algorithm have two completely opposite control result change trends aiming at the same two parameters, and once the difference value exceeds the allowable fluctuation error of the algorithm, the instability of the algorithm becomes high and early warning is needed.
In step S5, a target control signal is calculated according to the following formula:
in the formula (3), epsilonjThe maximum allowable parameter adjustment threshold value between the main control signal and the complex control signal corresponding to the jth regulation and control equipment; z is a radical of0Is the abnormal reporting signal, f (-) is the parameter correction function.
For control signals in which the switch class device is targeted: if ai(t)＝bi(t), any one of the signals may be used, if ai(t)≠biAnd (t), the sensing monitoring system is determined to receive two distinct control instructions aiming at the same target equipment, and manual or other third-party equipment is required to be applied for assistance processing. Taking the exhaust fan as an example, after the first control module processes the concentration of the hazardous gas in the power pipe gallery, the first control module judges that the exhaust fan needs to be started immediately, and outputs a starting signal to the exhaust fan; and if at the same time, the second control module judges that the dangerous gas concentration in the power pipe gallery is still in the safety interval after being processed, and still outputs a closing signal. Transmission deviceThe switch module of sense monitored control system to the exhaust fan receives and opens and close two completely different control instruction, applies for artifical or other third party equipment assistance processing this moment.
For control signals in which the control signal is for a regulatory class device: if α isj(t) and betaj(t) if the absolute value of the difference between the two control signals does not exceed the allowable parameter error of the corresponding control equipment, one control signal can be arbitrarily selected as an execution signal, or the two control signals can be combined to generate a new execution signal, otherwise, if alpha is not greater than the allowable parameter error of the corresponding control equipment, the control signals are combined to generate a new execution signal, and if alpha is greater than the allowable parameter error of the corresponding control equipment, the control signals are combined to generate a new execution signalj(t) and betajAnd (t) the absolute value of the difference value between the absolute values exceeds the allowable parameter error of the corresponding regulating and controlling equipment, and at the moment, the sensing and monitoring system is determined to receive two different control instructions aiming at the same target equipment and apply manual or other third-party equipment for assisting processing. Similarly, taking the exhaust fan as an example, after the first control module processes the concentration of the hazardous gas in the power pipe gallery, the first control module judges that the output power of the exhaust fan needs to be increased greatly at once, and outputs a power adjusting signal to the exhaust fan; and if same moment, the second control module is after handling the hazardous gas concentration in the electric power piping lane, and the current power of judging the exhaust fan is feasible or can also suitably reduce in order to reach energy-conserving purpose. That is, the sensing and monitoring system receives two control instructions with a large target output power difference for the power adjustment module of the exhaust fan, and requests manual or other third-party equipment to assist in processing.
Different generation factors of the control command are various, for example, the communication quality of one communication line is poor, or the accuracy of the control algorithm is reduced due to hardware reasons. In order to ensure that the power pipe lane always executes the correct control instruction, a human or other third party device is applied for assistance in processing once there is an unacceptable difference between the two signals. Therefore, in the embodiment, the functions of the main control signal and the multiple control signal are not only redundant control, but also mutual verification and important abnormity judgment, so that the technical problem that an error instruction is difficult to find due to the lack of a screening module in the prior art is effectively solved.
In this embodiment, a specific application mode for generating a new execution signal by combining two control signals is proposed by combining the self-error of the calculated algorithm and the error relationship between the two:
when Δ εA(T)×ΔεAB(T)＜0，|ΔεA(T)|＜|ΔεB(T) l, the stability of the main control signal A (T) is better, and alpha can be directly selectedj(t); similarly, when Δ εA(T)×ΔεAB(T)＞0，|ΔεA(T)|＜|ΔεBWhen (T) is less, the stability of the complex control signal B (T) is better, and beta can be directly selectedj(t); for other cases, it is stated that the error tendencies of the main control signal a (t) and the complex control signal b (t) relative to the standard signal may be opposite, and the error generating weight factors of the two (only one weight manner in the formula) may be combined, and then the weighted control signal may be calculated by combining the main control signal a (t) and the complex control signal b (t).
The determination process is detailed in Table 1, wherein, Delta epsilonA(T)＝A′(T)-A(T)，ΔεAB(T)＝A′(T)-B(T)， ΔεB(T)＝B′(T)-B(T)。
TABLE 1 error trend enumeration table for master control algorithm and multiple control algorithm
The control optimization method of this embodiment is an optimization method for a target control signal in a normal state of a main control algorithm and a multiple control algorithm, that is, even if the main control signal and the multiple control signal fluctuate, they do not exceed corresponding error thresholds, and the finally obtained target control signal can be closer to a standard signal of a current operation and maintenance cycle by using the control optimization method of this embodiment.
The above are only preferred embodiments of the present invention, and the scope of the present invention is not limited to the above examples, and all technical solutions that fall under the spirit of the present invention belong to the scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (9)
1. The utility model provides a power pipe gallery sensing monitoring method based on examine accuse signal optimization, according to the relevant characteristic of power pipe gallery and sensing monitoring system's target sensing data, its characterized in that is right examine accuse signal and adopt at least including following optimization step, generate final target control signal, send the sensing monitoring system of power pipe gallery and carry out the early warning suggestion:
and S1, generating a screening signal: processing the collected target sensing data of the operation and maintenance monitoring system of the power pipe gallery, and synchronously generating corresponding main control signals by adopting a main control algorithm and a multiple control algorithm which are independent of each other
And a complex control signal
Sending the master control signal and the re-control signal to an operation and maintenance monitoring system of the power pipe gallery; in the formula (I), the compound is shown in the specification,
for first sensed data received by the first control module over the first communication link,
second sensing data received for a second control module via a second communication link, F1(. is a master control algorithm built into the first control module, F2() is a complex built-in on the second control moduleA control algorithm;
s2, constructing an initial test model and a review test model: correcting the inherent error parameters of the main control algorithm and the multiple control algorithm according to the relevant characteristics of the power pipe gallery, and constructing an initial test model and a multiple test model by combining the correction results;
s3, carrying out preliminary verification on the screening signal: the first sensing data in the current auditing period T is compared
Second sensing data
And a re-control signal B (T) is led into the initial test model, so that the initial test model calls a master control algorithm to carry out the second sensing data
Calculating to obtain a single-core control instruction A '(T), analyzing the single-core control instruction A' (T), the main control signal A (T) and the complex control signal B (T) by combining the corrected allowable error threshold of the main control algorithm, and calculating to obtain the self error delta epsilon of the main control algorithmA(T), and the error between the master control algorithm and the override algorithm, Delta epsilonAB(T)；
S4, the screening signal is verified by a double-check method: collecting the first sensing data in the current auditing period T
Second set of sensory data
And leading the master control signal A (T) into a review model, so that the review model calls a review control algorithm to the first sensing data
Calculating to obtain a double check control instruction B' (T), and calculating to obtain the self error of the double control algorithm by combining the corrected error parameter of the double control algorithmΔεB(T) judging the reliability of the screening signal, if the screening signal is reliable, entering the step S5, otherwise, generating an early warning signal, and ending the process;
s5, optimizing the discrimination control signal to obtain a target control signal: self error delta epsilon combined with master control algorithmA(T), error between master control algorithm and multiple control algorithmAB(T), self error Delta epsilon of complex control algorithmBAnd (T) estimating the error trend of the main control signals A (T) and the complex control signals B (T), and calculating to obtain the target control signals according to the estimation result.
2. The method for monitoring power pipe gallery sensing based on screening signal optimization of claim 1, wherein in the step S5, the process of estimating the error trend of the main control signal a (t) and the complex control signal b (t) comprises the following steps:
s51, fitting the main control signal A (T), the complex control signal B (T), the single-core control instruction A '(T) and the complex control instruction B' (T) of the current auditing period T by taking time as a horizontal axis and taking the signal normalization value as a vertical axis to obtain a corresponding fitting curve YA(T)、YB(T)、YA′(T) and YB′(T)；
S52, combining the fitting curve YA(T)、YB(T)、YA′(T) and YB′And (T) estimating the error trend of the main control algorithm and the complex control algorithm.
3. The screening signal optimization-based power pipe gallery sensing monitoring method according to claim 2, wherein in the step S52, the fitting curve Y is combinedA(T)、YB(T)、YA′(T) and YB′(T) estimating the error trend of the main control algorithm and the complex control algorithm is as follows:
selecting two fitting curves positioned in the middle position, and analyzing a corresponding control algorithm:
if the control algorithms corresponding to the two fitting curves in the middle position are both master control algorithms, judging that the error between the master control signal and the standard signal is smaller; if the control algorithms corresponding to the two fitting curves positioned in the middle position are both complex control algorithms, judging that the error between the complex control signals and the standard signals is smaller; otherwise, the error trend between the main control signal and the standard signal and the error trend between the complex control signal and the standard signal are opposite.
4. The screening signal optimization-based power pipe gallery sensing monitoring method according to claim 1, wherein the main control signal A (t) is:
a in formula (1)i(t) is a control signal of the master control algorithm for the ith switch class device, wherein i is 1, 2. Alpha is alphajAnd (t) is a control signal of the master control algorithm for the jth regulation device, and j is 1, 2.
5. The power pipe gallery sensing monitoring method based on screening signal optimization according to claim 4, wherein the complex control signal B (t) is:
b in formula (2)i(t) is a control signal which is sent by a complex control algorithm and aims at the ith switch class device, wherein i is 1, 2. Beta is a betajAnd (t) is a control signal which is sent by a complex control algorithm and aims at the jth regulation and control type equipment, and j is 1, 2.
6. The method for monitoring the power pipe gallery sensing based on the screening signal optimization as set forth in claim 5, wherein in step S5, the target control signal is calculated according to the estimation result by using the following formula:
in formula (3), εjThe maximum allowable parameter adjustment threshold value between the main control signal and the complex control signal corresponding to the jth regulation and control equipment; z is a radical of0Is an abnormal reporting signal, f (-) is a parameter correction function:
7. the method for sensory monitoring of a power pipe corridor optimized based on screening signals according to claim 1, characterized in that in step S2, the relevant characteristics Y of the power pipe corridorq(T) is:
Yq(T)＝{Eq(T)，ρq(T)，Γq(T)，τq(T)} (1)；
in the formula (3), q is the number of the power pipe gallery; eq(T) is the influence value of the power pipe gallery in the Tth audit period; rhoq(T) is the number of target facilities in the power pipe gallery in the Tth audit period; gamma-shapedq(T) is the time length of putting the power pipe gallery into operation until the Tth audit period; tau.qAnd (T) is the interval duration of last maintenance of the power pipe gallery until the Tth audit period.
8. The method for power pipe corridor sensing monitoring based on screening signal optimization according to claim 4, wherein in the step S3, the process of performing preliminary verification on the screening signal comprises the following steps:
s301, requesting the second control module to feed back the received second sensing data in the appointed auditing period
S302, second sensing data is processed
The first sensing data received by the first control module in the same auditing period
Comparing, and evaluating the communication quality of the first communication link according to the comparison error; if the comparison error between the two meets a preset comparison error threshold value, calling a master control algorithm to second sensing data
Processing to generate corresponding single-core control instruction
Entering step S303; otherwise, directly outputting the communication quality evaluation result of the first communication link as an initial test result D (T), generating an early warning signal, and ending the process;
s303, calling a signal comparison function g (-) to perform matching calculation on the single-core control instruction A' (T) and the main control instruction A (T) in the same auditing period to obtain the self error delta epsilon of the main control algorithmA(T)＝g(A′(T)，A(T)，εjA)；εjAIs the allowable error threshold of the modified master control algorithm;
s304, calling a signal comparison function g (-) to compare and analyze the single-core control instruction A' (T) and the complex control instruction B (T) in the appointed auditing period to obtain an error delta epsilon between the master control algorithm and the complex control algorithmAB(T)＝g(A′(T)，B(T)，εjAB)；εjABIs the allowable error threshold between the modified main control algorithm and the complex control algorithm;
s305, integrating the communication quality evaluation result of the first communication link in the current auditing period T and the error stability Delta epsilon of the master control algorithmA(T) and the absolute value of the error between the master control algorithm and the multiple control algorithmAB(T), generating an initial test result D (T).
9. The method for sensing and monitoring the power pipe corridor based on the screening signal optimization according to claim 4, wherein in the step S3, the process of verifying the screening signal comprises the following steps:
s311, requesting the first control module to feed back the received first sensing data in the appointed auditing period
S312, the first sensing data is processed
Second sensing data of the same auditing period
Comparing, evaluating the communication quality of the second communication link according to the comparison error, and calling a re-control algorithm to the first sensing data if the comparison error between the first communication link and the second communication link meets a preset comparison error threshold value
Processing to generate corresponding double check control instruction
Proceeding to step S313; otherwise, directly outputting the communication quality evaluation result of the second communication link as a re-checking result F (T), generating an early warning signal, and ending the process;
s313, calling a signal comparison function g (-) to perform matching calculation on the double-check control instruction B' (T) and the double-control instruction B (T) in the same auditing period to obtain the self error delta epsilon of the double-control algorithmB(T)＝g(B′(T)，B(T)，εjB)；εjBIs the allowable error threshold of the modified complex control algorithm;
s314, using a master control algorithm F1And multiple control algorithm F2Maximum allowable error value epsilon ofjBARandomly generating a certain amount as a criterionTo a part of the first sensed data
Replacing to obtain standard check data
T＝{t1，t1，...，tK}，tkIs the kth acquisition time node of the current audit period T, K is 1, 21，t1，...，tK}；
S315, respectively calling the master control algorithm and the repeated control algorithm to check the standard data
Processing to generate check control signal A*(T) and a check reset signal B*(T)；
S316, for each acquisition time node tkCheck-up of (A) complex control signal*(tk) And check complex control signal B*(tk) Comparing, analyzing and obtaining the position information Y e { t of the error sensing data1，t1，...，tKMatching the position information Y with the replacement position information X to obtain a matching result f2(t)；
S317, combining the communication quality evaluation result of the second communication link and the self error delta epsilon of the multiple control algorithmB(T) and matching result f2(t) generating a review result F (T).
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007195318A (en) * | 2006-01-19 | 2007-08-02 | Chugoku Electric Power Co Inc:The | Information management and communication device, information communication processor, and system and method for processing power equipment information |
| CN103345222A (en) * | 2013-06-26 | 2013-10-09 | 中国南方电网有限责任公司 | Control method and system for power grid operation |
| CN103532963A (en) * | 2013-10-22 | 2014-01-22 | 中国联合网络通信集团有限公司 | IOT (Internet of Things) based equipment authentication method, device and system |
| CN103606048A (en) * | 2013-11-22 | 2014-02-26 | 国家电网公司 | Relay protection on-line rechecking method and system based on OMS |
| WO2016028710A1 (en) * | 2014-08-18 | 2016-02-25 | The Regents Of The University Of California | Electricity monitoring devices and systems, and methods of use thereof |
| CN105373062A (en) * | 2015-12-22 | 2016-03-02 | 上海天跃科技股份有限公司 | Monitoring and alarm device |
| US20160154693A1 (en) * | 2013-07-02 | 2016-06-02 | Phoenix Contact Gmbh & Co.Kg | Method for error monitoring, control and data transmission installation and control device |
| US20170254546A1 (en) * | 2014-09-16 | 2017-09-07 | Better Watt | Apparatus for monitoring at least one thermal control device, and associated control unit and control system |
| CN207752313U (en) * | 2017-10-24 | 2018-08-21 | 上海电器科学研究所（集团）有限公司 | A kind of cities and towns pipe gallery strange land redundant manipulator based on real-time ethernet technology |
| CN108960621A (en) * | 2018-06-27 | 2018-12-07 | 中南大学 | The bridge monitoring and evaluation method of high-speed rail bridge are worn under a kind of shield tunnel construction |
| CN110274630A (en) * | 2019-07-02 | 2019-09-24 | 凯铭诺(深圳)科技有限公司 | A kind of low-voltage distributing line branch monitoring device and its monitoring method |
| CN215067841U (en) * | 2021-08-06 | 2021-12-07 | 海南大学 | Underground pipe gallery safety positioning and monitoring system based on CAN bus |
-
2021
- 2021-12-14 CN CN202111528950.8A patent/CN114779673B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007195318A (en) * | 2006-01-19 | 2007-08-02 | Chugoku Electric Power Co Inc:The | Information management and communication device, information communication processor, and system and method for processing power equipment information |
| CN103345222A (en) * | 2013-06-26 | 2013-10-09 | 中国南方电网有限责任公司 | Control method and system for power grid operation |
| US20160154693A1 (en) * | 2013-07-02 | 2016-06-02 | Phoenix Contact Gmbh & Co.Kg | Method for error monitoring, control and data transmission installation and control device |
| CN103532963A (en) * | 2013-10-22 | 2014-01-22 | 中国联合网络通信集团有限公司 | IOT (Internet of Things) based equipment authentication method, device and system |
| CN103606048A (en) * | 2013-11-22 | 2014-02-26 | 国家电网公司 | Relay protection on-line rechecking method and system based on OMS |
| WO2016028710A1 (en) * | 2014-08-18 | 2016-02-25 | The Regents Of The University Of California | Electricity monitoring devices and systems, and methods of use thereof |
| US20170254546A1 (en) * | 2014-09-16 | 2017-09-07 | Better Watt | Apparatus for monitoring at least one thermal control device, and associated control unit and control system |
| CN105373062A (en) * | 2015-12-22 | 2016-03-02 | 上海天跃科技股份有限公司 | Monitoring and alarm device |
| CN207752313U (en) * | 2017-10-24 | 2018-08-21 | 上海电器科学研究所（集团）有限公司 | A kind of cities and towns pipe gallery strange land redundant manipulator based on real-time ethernet technology |
| CN108960621A (en) * | 2018-06-27 | 2018-12-07 | 中南大学 | The bridge monitoring and evaluation method of high-speed rail bridge are worn under a kind of shield tunnel construction |
| CN110274630A (en) * | 2019-07-02 | 2019-09-24 | 凯铭诺(深圳)科技有限公司 | A kind of low-voltage distributing line branch monitoring device and its monitoring method |
| CN215067841U (en) * | 2021-08-06 | 2021-12-07 | 海南大学 | Underground pipe gallery safety positioning and monitoring system based on CAN bus |
Non-Patent Citations (3)
| Title |
|---|
| G. WANG: "Multi-source Heterogeneous Data Fusion Method for Pipe Gallery Condition Monitoring", 《2020 IEEE 9TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE》, pages 1359 - 1363 * |
| 余文杰: "通信系统对电力系统稳定控制的影响研究", 《中国优秀博士学位论文全文数据库工程科技II辑》, pages 042 - 161 * |
| 周正兵: "公路隧道火灾安全监控系统研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, pages 140 - 306 * |
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